Stain-free histopathology by chemical imaging

ABSTRACT

The present disclosure provides methods, systems, and computer-readable storage media that can be used to image an unstained sample. Traditionally histopathology and immunohistochemistry methods use stains or dyes in combination with microscopy (or other detection methods) to detect cells and cellular structures, such as proteins. However, the disclosed methods do not require the use of such stains and dyes. The disclosed methods can include obtaining a spectroscopic image (e.g., infrared (IR) imaging data) of the sample, analyzing the resulting spectroscopic image to reduce the dimensionality of the spectroscopic image, comparing the reduced spectroscopic image compared to a control (e.g., by using an appropriately trained algorithm) and generating an output computed stain image from the reduced IR spectra, thereby imaging the sample without the use of stains or dyes.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 14/211,292 filed Mar. 14, 2014, which claims thebenefit of U.S. Provisional Application No. 61/794,171, filed Mar. 15,2013. The contents of each of the foregoing are hereby incorporated byreference into this application as if set forth herein in full.

ACKNOWLEDGMENT OF GOVERNMENT SUPPORT

This invention was made with government support under 1R01EB009745awarded by The National Institutes of Health. The government has certainrights in the invention.

FIELD

The disclosure provides methods of imaging biological samples to providea molecular composition of the sample without the use of stains.

BACKGROUND

Clinical decisions and biomedical research rely significantly on imagingthe architecture and morphology of tissues. Unfortunately, tissues havelittle contrast in brightfield optical imaging (FIG. 1A), and requirethe use of stains or dyes to provide contrast. Contrast agents includethose that highlight morphology as well as those that highlight specificmolecular species, usually using immunohistochemical (IHC) techniques(Dabbs, Diagnostic Immunohistochemistry: Theranostic and GenomicApplications—Expert Consult., Elsevier Health Sciences, 2010). The useof staining is especially critical in histopathologic analyses that arethe gold standard for the diagnoses of many diseases, including cancer,and for most tissue research. Usually, the pathologist examines tissuearchitecture and histology (cell types) to provide an initial diagnosiswhich may be augmented by modern computerized analyses (Camp et al., NatMed 8:1323-8, 2002). In some cases, confirmatory IHC staining may beemployed to determine appropriate therapy or to improve diagnosticaccuracy or to guide appropriate therapy. While advances have been made(Ruifrok et al., Anal Quant Cytol Histol 23:291-9,2001; Taylor et al.,Histopathology 49:411-24, 2006), the time required for staining andexpense of obtaining multiple stains can be a limiting factor andinconsistent staining due to a variety of technological and tissuefactors is problematic (Goldstein et al., Appl. Immunohistochem. Mol.Morphol. 15:124-33, 2007). Staining patterns in some cases need to beinterpreted in the context of multiple stains or appropriate morphologicvisualization to be effective (Varma et al., Histopathology 47:1-16,2005), compounding the need for multiplex marker staining and furtherinterpretation.

SUMMARY

Provided herein are methods and systems for generating or obtaining animage of a sample, such as biopsy tissue sections, without the use ofdyes or stains (such as those currently used in histopathology and IHC).Spectroscopic imaging (such as Fourier transform infrared (FT-IR)spectroscopic imaging) is used to record the intrinsic chemicalcomposition of sample, and numerical algorithms are employed to relatethe biochemical content of the IR spectral data to structure of thesample, such as tissue structure or disease-relevant information (e.g.,protein expression). This results in accurate histologic and pathologicclassification, which is comparable in terms of the staining patterns ina sample (e.g., tissue) to what would be achieved if stains or dyes wereused instead. Thus, the methods generate computationally-stained imagesof samples that allow both morphologic visualization currently enabledby dyes and molecular expression currently enabled by immunostaining(e.g., by using labeled antibodies).

The disclosed methods of spectroscopic imaging of unstained samplesallow tissue or other samples to be classified based on quantitative andreproducible optical measurements of tissue properties and are notsubject to staining or IHC irregularities. Small or large samples,multiple epitopes and single data acquisition can be used to perform avariety of assays. The disclosed computed staining methods and systemsprovide more robust input for automated diagnosis methods, for exampleby eliminating the need to perform color correction, sample-to-samplevariation and adjust for staining artifacts.

In some examples, the methods of imaging an unstained sample includeobtaining a spectroscopic image of the sample, for example an IRabsorption image, thereby generating chemical information on every pixelin the data. Each pixel contains a spectrum. For example, an IRabsorption image can be associated with an IR spectrum at every pixel.The resulting spectroscopic image is analyzed to reduce thedimensionality of the spectroscopic image, thereby generating a reducedspectroscopic image (e.g., reduced IR spectra). The reducedspectroscopic image can be compared to a control, for example byapplying or inputting the image into an algorithm containing a set ofparameters defining the network (wherein the algorithm was generatedwith appropriately stained control samples). The control can be obtainedusing existing molecular methods in pathology, including IHC stainingand blotting analyses. Pathology methods provide information on whetherthe molecule of interest is present and the relative strength of itsexpression. The level of staining is correlated with specificspectroscopic features (such as IR spectra). Hence, each pixel can beassigned a stain level from the spectroscopic image (e.g., IR spectrum).Subsequently, an output computed stain image from the reducedspectroscopic image (e.g., reduced IR spectra) is generated, therebyimaging the sample without the use of stains or dyes.

In some examples the method can further include treating a subjectidentified as having a particular disease, such as a particular canceror infection.

In some examples the method can further include selecting a subjectsuspected of having a particular disease, such as a particular cancer orinfection and obtaining a sample from the subject.

The disclosure also provides systems for imaging an unstained sample.Such a system can include a means for obtaining a spectroscopic image ofthe unstained sample, implemented rules for reducing dimensionality ofthe spectroscopic image, implemented rules for comparing the reducedspectroscopic image to a control image (such as a stored control imageor plurality of images), and means for implementing the rules, therebygenerating an output computed stain image from the reduced spectroscopicimage and imaging the unstained sample.

Also provided are computer-readable storage medium having instructionsthereon for performing the disclosed methods, such as methods of imaginga sample without the use of dyes or stains.

The foregoing and other objects and features of the disclosure willbecome more apparent from the following detailed description, whichproceeds with reference to the accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIGS. 1A-1E show how the disclosed methods can be used in place ofstaining tissue samples. (A) Brightfield microscopy image of anunstained tissue section. (B) Tissue is stained, commonly withhematoxylin and eaosin (H&E), to visualize tissue morphology. (C)Spectroscopic imaging data recorded from unstained tissue, whichincludes a spectrum at every spatial location or an image at everychemically-specific feature in the spectrum (e.g., stack ofchemically-specific images). (D) The data (underlying chemicalcomposition) can be converted to conventional pathology images in anobjective and automated manner using recognition algorithms. Forexample, in (E) the H&E stained image (similar to B) is reproducedwithout staining.

FIGS. 2A-2E are digital images showing that molecular imaging (threesample panel on the left) can be reproduced by chemical imaging (threesample panel on the right). In addition to H&E stained images (A), themethod of stainless staining can also be used in place ofmolecularly-specific stains, including (B) Masson's Trichrome stain(collagen and keratin fibers) (C) high molecular weight (HMW)cytokeratin (epithelial-type cell), (D) smooth muscle alpha actin(myo-like cell) and (E) vimentin (fibroblast-like cell). Each spot is1.4 mm in diameter.

FIGS. 3A-3G are digital images demonstrating that the same sample can be“stained” with many different computational “stains” while alsoproviding consistently uniform, high-quality results. (A)physically-stained H&E image of a series of samples and thecorresponding images derived from an unstained sample (B) chemical imageat the Amide I vibrational mode, (C) computationally-stained H&E image,(D) computationally-stained Masson's trichrome image, (E)computationally-stained cytokeratin image. The scale bar represents 800μm. For fields of view typical under a microscope, the digital stains(F, bottom) help prevent artifacts sometimes observed in conventionalH&E staining (F, top). The scale bar represents 700 μm. Comprehensivestaining is possible for tumor resections (G) where multiple stainedimages can be generated from the sample for different regions and atdifferent scales. Images can be overlaid, merged or multiplyhighlighted. The scale bar represents 2.6 mm.

FIG. 4 provides an overview of the disclosed methods. The test sample isprovided, and then and spectroscopic image (e.g., IR spectra) from thesample is obtained 110. The resulting spectroscopic image is analyzed toreduce its complexity 112. The reduced spectroscopic image (e.g.,reduced IR spectra) is compared to a control database 114, and then anoutput compute stain image is generated 116.

DETAILED DESCRIPTION

Unless otherwise explained, all technical and scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which a disclosed invention belongs. Unlessotherwise explained, all technical and scientific terms used herein havethe same meaning as commonly understood by one of ordinary skill in theart to which this disclosure belongs. The singular terms “a,” “an,” and“the” include plural referents unless context clearly indicatesotherwise. Similarly, the word “or” is intended to include “and” unlessthe context clearly indicates otherwise. “Comprising” means “including”;hence, “comprising A or B” means “including A” or “including B” or“including A and B.” All references cited herein are incorporated byreference.

Bacteria: Prokaryotic organisms that in some examples cause disease(pathogenic bacteria). Bacteria can be classified based on thestructural characteristics of their cell walls. For example, the thicklayers of peptidoglycan in the “Gram-positive” cell wall stain purple,while the thin “Gram-negative” cell wall appears pink.

Cancer: Malignant neoplasm, for example one that has undergonecharacteristic anaplasia with loss of differentiation, increased rate ofgrowth, invasion of surrounding tissue, and is capable of metastasis.

Detect: To determine if an agent (such as a signal, protein, cellularstructure, organism, or cell) is present or absent, for example a tumorcell. In some examples, this can further include quantification. Forexample, use of the disclosed methods in particular examples permitsreporting of particular morphology and expression in a sample.

Control: A sample or standard used for comparison with an experimentalor test sample. In some embodiments, the control is a normal sampleobtained from a healthy patient (or plurality of patients), such as asample or plurality of samples for subjects without a tumor or who areknown to not be infected with a target pathogen. In some examples,images of control samples are stored in a database.

In some embodiments, the control is a historical control or standardreference value or range of values (such as a previously tested controlsample(s), such as a known cancer, normal sample, or benign sample). Insome embodiments the control is a standard value representing theaverage value (or average range of values) obtained from a plurality ofpatient samples, such as known normal samples or known cancer samples.

Diagnose: The process of identifying a medical condition or disease, forexample from the results of one or more diagnostic procedures. Inparticular examples, diagnosis includes determining whether a sampleobtained from a subject is a tumor, contains a particular pathogen(e.g., bacterium, virus, parasite, or fungus), or expresses a targetprotein (such as a tumor associated antigen, e.g., BRCA, CA-125, CEA).

Normal sample, cells or tissue: Non-tumor, non-malignant cells andtissue, as well as those not containing the target pathogen, cell, orprotein.

Sample: A sample, such as a biological sample, includes those samplesobtained from a subject or from an environmental sample, which caninclude cells, nucleic acids, and/or proteins. As used herein,biological samples include all clinical samples useful for detection ofa cell or a disease, such as an infection or a tumor in subjects,including cells, cell lysates, cytocentrifuge preparations, cytologysmears, tissue biopsies (e.g., core biopsy), fine-needle aspirates,tissue sections (e.g., cryostat tissue sections and/or paraffin-embeddedtissue sections), and fluid samples. Samples include but are not limitedto, cells, tissues, and bodily fluids, such as a tissue or tumor biopsy,fine needle aspirate, a core biopsy sample, an excisional biopsy sample,bronchoalveolar lavage, pleural fluid, spinal fluid, saliva, sputum,surgical specimen, lymph node fluid, ascites fluid, peripheral blood orfractions thereof (such as serum or plasma), urine, saliva, buccal swab,vaginal swab, breast milk, and autopsy material. Samples includebiopsied or surgically removed tissue, including tissues that are, forexample, unfixed, frozen, fixed in formalin and/or embedded in paraffin.In some examples, a tissue sample is a fresh sample, frozen sample, orfixed sample (e.g., embedded in paraffin).

Slide: Traditionally a substrate used to mount or attach a sample to formicroscopy, which is typically but not necessarily transparent to light.Samples may be processed before and/or after mounting onto a slide. Insome examples, a slide may be more or less transparent or opaque andmade of glass, silica, quartz, or any other material amenable toGram-staining. A slide may be configured to accommodate one or morespecimens from one or more subjects. A slide as used herein alsoincludes non-traditional substrates such as a tape, a disc, a plate, orany other flat, curved, rectangular, or round surface or shape amenableto presenting a sample for analysis using the disclosed methods.

Stain: A dye or other label used in microscopy, for example to enhancecontrast in the microscopic image or to detect particular structures inbiological tissues, such as cell populations (e.g., cancerous cells),organelles within cells, particular proteins, particular nucleic acids,lipids, or carbohydrates). Exemplary stains used in histology, cytology,haematology and research include but are no limited to: H&E, iodine,methylene blue, eosin Y, Congo Red, carmine, toluidene blue, Wright'sstain, crystal violet, aceto-orcein, Sudan III, and the like.

1,4-Phenylenediamine; 2,3,5-triphenyltetrazolium chloride;2,4-Dinitro-5-fluoroaniline; 2-Naphthol (beta); 3,3′-diaminobenzidine;4-chloro-1-naphthol; 4-Chloro-1-naphthol; acridine orange; silverproteinate; Alcian Blue 8GX; lizarin; alizarin Red S; Alkali Blue 4 B;ammonium molybdate tetrahydrate; aniline hydrochloride; auramine O;azocarmine B; azocarmine G; azophloxine; azure A; azure B; azure II;azure II-eosin; azure mixture sicc. Giemsa Stain; bengal Rose B,benzopurpurine 4B; Prussian Blue, Bismarck Brown Y (G); Bismarck BrownR; bismuth(III) nitrate basic; lead(II) acetate Trihydrate; lead(II)citrate trihydrate; lead(II) nitrate; lead(II) tartrate; leadtetraacetate; borax Carmin solution; brilliant green; brilliant cresylblue; bromocresol green; bromocresol Green Sodium salt; bromocresolpurple; bromophenol blue; bromosulfalein; bromothymol blue;Carbol-Fuchsin; carbol-gentianaviolet solution; carbol-Methylene BlueSolution; carmine; celestine blue; quinacrine mustard dihydrochloride,quinoline yellow; chlorazol black; chromium(VI) oxide; chromotrop 2 R;chrysoidine G; cobaltous chloride; cobalt naphthenatel cyanosine;cytochrome c; direct red; direct red 80; fast blue B; fast blue BB; fastblue RR; fast green; fast red 3 GL; fast red RC; fast violet B; eosin;eosin yellowish; eosin-hematoxylin; eosin methylene blue; eosin scarlet;eriochrome red B; erythrosin extra bluish; acetic acid; ethyl violet;Evans blue fluka; ferritin; fat red bluish; fat black for microscopy;fluorescein isothiocyanate; fuchsin; gallocyanine; gentian violet;Giemsa-Solution; gold; hemalaun; hematein; hematoxylin; Hanker-YatesReagent; Hayem's Solution; hesperidin; indigocarmine; indium(III)chloride; iodonitrotetrazolium chloride; iso-chloridazon; Janus Green B;potassium dichromate; potassium hexahydroxoantimonate (V); potassiumpermanganate; carmine Solution; nuclear fast red; Congo Red; cresol red;cresyl violet; crystal violet; lactophenol blue; lanthanum nitratehexahydrate; light green SF yellowish; lipid crimson; lithium carbonate;Lugol Solution; malachite green oxalate; May-Grünwald Solution; metanilYellow; methylene blue; methylene green zinc double salt; methyl Green;methyl orange; methyl violet; morin; mucicarmine;N-(4-Amino-2,5-diethoxyphenyl)benzamide; N,N-dimethylaniline;N,N-dimethyl-p-toluidine; Naphthol AS-acetate; various Naphthol dyessuch as naphthol AS-TR-phosphate, naphthol blue black, naphthol yellowS, and naphthol green B; sodium tungstate dihydrate; neotetrazoliumchloride; new Coccine; new fuchsine; new methylene blue N; neutral red;nigrosin B; Nile Blue A; Nile Blue chloride; ninhydrin; nitrazineyellow; nitrotetrazolium blue chloride; orange G; orcein; palladium(II)chloride; palladium(II) oxide; parafuchsin; peroxidase; phenosafranine;phosphomolybdic acid; phosphorus pentoxide; phosphotungstic acid;phthalocyanine; picric acid; pinacyanol iodide; platinum(IV) oxidehydrate; ponceau BS; ponceau S; pyridine; pyronine Y (G); resorufin;rhodamine B; ruthenium(III) chloride anhydrous; ruthenium red; safraninT; Safranin Solution acc. to Olt; acid fuchsin; scarlet R; Schiff'sreagent; silver; silver nitrate; Sirius Rose BB; Sudan Blue II; SudanOrange G; Sudan Red B; Sudan Black B; sulforhodamine B acid chloride;tartrazine; tetranitroblue tetrazolium chloride; tetrazolium Bluechloride; tetrazolium Violet; thallium(I) nitrate; thiazole Yellow G;thiazolyl blue tetrazolium bromide; thiocarbohydrazide; thioflavine;thionine acetate; toluidine blue; tropaeolin 000 No. 1 and No. 2; trypanBlue; Tuerk Solution; uranyl acetate dihydrate; uranyl nitrateHexahydrate; variamine Blue B salt; vesuvine Solution acc. to Neisser;Victoria Blue B; water blue; Weigert's Solution; Tungstosilicic acidhydrate; Wright Stain; and xylenecyanol FF.

Stains also include detectable labels attached to proteins (e.g.,antibodies) or nucleic acid molecules, to permit detection of targetproteins or nucleic acids. Exemplary labels include but are not limitedto haptens, fluorophores, radioisotopes enzymes, and quantum dots.

The disclosed methods do not use stains to image samples, but stillpermit detection of particular structures in biological tissues, such ascell populations (e.g., cancerous cells), organelles within cells,particular proteins, particular nucleic acids, lipids, carbohydrates,and the like.

Subject: Includes any multi-cellular organism, such as a vertebrate ormammal, such as human and non-human mammals (e.g., veterinary subjects,such as dogs, cats, mice, rodents, cows, pigs and the like). In someexamples, a subject is one who has or is suspected of having a disease,such as a tumor, or of being infected with a pathogen, such as abacterial infection. Biological samples from subjects can be analyzedusing the disclosed methods.

Tumor: An abnormal growth of cells, which can be benign or malignant.Cancer is a malignant tumor, which is characterized by abnormal oruncontrolled cell growth. Other features often associated withmalignancy include metastasis, interference with the normal functioningof neighboring cells, release of cytokines or other secretory productsat abnormal levels and suppression or aggravation of inflammatory orimmunological response, invasion of surrounding or distant tissues ororgans, such as lymph nodes, etc. “Metastatic disease” refers to cancercells that have left the original tumor site and migrate to other partsof the body for example via the bloodstream or lymph system. A tumorthat does not metastasize is referred to as “benign.”

Examples of hematological tumors include leukemias, including acuteleukemias (such as 11q23-positive acute leukemia, acute lymphocyticleukemia, acute myelocytic leukemia, acute myelogenous leukemia andmyeloblastic, promyelocytic, myelomonocytic, monocytic anderythroleukemia), chronic leukemias (such as chronic myelocytic(granulocytic) leukemia, chronic myelogenous leukemia, and chroniclymphocytic leukemia), polycythemia vera, lymphoma, Hodgkin's disease,non-Hodgkin's lymphoma (indolent and high grade forms), multiplemyeloma, Waldenstrom's macroglobulinemia, heavy chain disease,myelodysplastic syndrome, hairy cell leukemia and myelodysplasia.

Examples of solid tumors, such as sarcomas and carcinomas, includefibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenicsarcoma, and other sarcomas, synovioma, mesothelioma, Ewing's tumor,leiomyosarcoma, rhabdomyosarcoma, colon cancer, rectal cancer, analcancer, lymphoid malignancy, pancreatic cancer, breast cancer (includingbasal breast carcinoma, ductal carcinoma and lobular breast carcinoma),lung cancers, ovarian cancer, uterine cancer, prostate cancer,hepatocellular carcinoma, squamous cell carcinoma, basal cell carcinoma,adenocarcinoma, sweat gland carcinoma, medullary thyroid carcinoma,papillary thyroid carcinoma, pheochromocytomas sebaceous glandcarcinoma, papillary carcinoma, papillary adenocarcinomas, medullarycarcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bileduct carcinoma, choriocarcinoma, Wilms' tumor, cervical cancer,testicular tumor, seminoma, bladder carcinoma, and CNS tumors (such as aglioma, astrocytoma, medulloblastoma, craniopharyrgioma, ependymoma,pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma,meningioma, melanoma, neuroblastoma and retinoblastoma).

In specific examples, the tumor is melanoma, breast cancer, prostatecancer, esophageal cancer, liver cancer, gastrointestinal cancer, coloncancer, rectal cancer, or a lung carcinoma. In another example, a tumoris a skin tumor. In another example, a tumor is a papilloma.

Suitable methods and materials for the practice and/or testing ofembodiments of the disclosure are described below. Such methods andmaterials are illustrative only and are not intended to be limiting.Other methods and materials similar or equivalent to those describedherein also can be used. For example, conventional methods well known inthe art to which a disclosed invention pertains are described in variousgeneral and more specific references.

Overview of the Technology

Staining tissue to highlight its morphology or molecular content isstandard practice in most clinical diagnoses and biomedical research(Kumar, V., Abbas, A. K., Fausto, N. & Aster, J. Robbins and CotranPathologic basis of disease. Saunders, 200)). While dyes such ashematoxylin and eosin (H&E) have been commonly used for over a hundredyears, more recently, specific epitopes are used to visualize molecularmarkers in research and clinical practice (Bratthauer, et al. Hum.Pathol. 33:620-7, 2002). The disclosure provides a third method forimaging samples, for example using microscopy, without the need forstains or dyes (labels) commonly used in histopathology,immunohistochemical (IHC) and in situ hybridization (e.g., FISH, SISH)methods. In this approach, spectroscopy is used to record the intrinsicchemical composition of tissue and numerical algorithms are employed torelate the biochemical content of the data to tissue composition. Themethod generates computationally-“stained” images of tissue that allowsfor accurate morphologic visualization and high fidelity molecularexpression normally afforded by dyes and immunostaining, respectively.Since the tissue is not actually stained and the histopathologicinformation is algorithmically obtained, this method can be referred toas stain-less staining, stain-free chemical imaging, or stainlesscomputed molecular histopathology.

Stainless computed histopathology enables a rapid, quantitative andnon-perturbing visualization of morphology and multiple molecularepitopes simultaneously. The disclosed methods can provide morphologicalvisualization and molecular expression, for example used in clinicaldiagnoses and biomedical research. Currently available imaging methodsused for clinical decisions and biomedical research often rely onimaging a tissue's architecture and morphology. However, because tissuesdo not have enough contrast in brightfield optical imaging, currentlyavailable methods rely on stains and dyes to add contrast.Unfortunately, the use of stains and dyes requires time and expense, andthere are issues with inconsistent staining between samples andlaboratories, for example due to technological and tissue factors.

In the disclosed methods, spectroscopy is used to record the intrinsicchemical composition of tissue and algorithms are employed to relate thebiochemical content of the data to tissue structure or disease-relevantinformation. The methods generate computationally-stained images oftissue that allow both morphologic visualization currently enabled bydyes and molecular expression currently enabled by immunostaining. Sincethe tissue is not actually stained and the histopathologic informationis algorithmically obtained, the sample can be analyzed numerous times(potentially limitless numbers of times), without any loss in sampleintegrity. Stainless computed pathology provides a rapid, quantitativeand non-perturbing visualization of morphology and multiple molecularepitopes for research and clinical tasks.

As opposed to imaging with dyes or stains, the disclosure provides analternative in the form of chemical imaging (Lewis et al., Anal. Chem.67:3377-81, 1995; Evans et al., PNAS 102:16807-12, 2005; Ifa et al.,Science 321:805, 2008; and De Smit et al., Nature 456:222-5, 2008). Themethods provided in these references teach how to generate chemicalcontrast in tissue or samples. However, the methods provided therein donot teach how to obtain molecular contrast that is traditionallyobtained by staining. In chemical imaging, optical imaging is combinedwith spectroscopy to provide both the morphologic detail of microscopyand the molecular selectivity of spectroscopy. In particular, theoptical frequencies of the mid-infrared region of the spectrum are inthe range of molecular vibrational frequencies. Hence, if a molecularspecies is present, light is absorbed in a pattern consistent with itsmolecular constitution. While a microscope provides straightforwardimaging capability, (Bhargava, Appl. Spectrosc. 66:1091-1120, 2012) theabsorption spectrum can be used as a pattern of composition as well as areadout of metabolic activity (Ellis et al., Analyst 131:875-85, 2006).Combined with numerical algorithms, the data can be used to recognize arange of structures used for research and clinical applications,including bacteria and other pathogens, isolated aberrant cells, tissuestructures and disease (such as a tumor or cancer) (Jamin et al., PNAS95:4837-4840, 1998; Bellisola et al., Analyst 135:3077-86, 2010 and Diemet al., Analyst 129:880-5, 2000). The contrast mechanism betweendifferent components in a sample is straightforward and instrumentationis readily available and underlying principles are largely understood.While the data have been used by numerous groups to relate spectralproperties to cell type and disease state, the belief has largely beenthat this information is in addition to or complementary to conventionallaboratory and clinical analyses (Biomedical Vibrational Spectroscopy.John Wiley & Sons, 2008). Here it is shown that the data can also beused to generate the same information as classical dyes and variousmolecular stains.

For example, the transformation shown in FIGS. 1A-1B is the current wayof visualizing tissues using brightfield microscopy (FIG. 1A) and H&Estaining (FIG. 1B) of samples. In contrast, FIGS. 1C-1E show how thedisclosed methods can be used to obtain an image (FIG. 1E) that lookssimilar to the H&E image (FIG. 1B), without staining the tissue.

Providing multiple, registered epitopes can overcome shortcomings inautomated algorithms (Beck et al., Sci. Transl. Med.3:108ra113-108ra113, 2011). It is shown therein that many stains can bereplaced with computed histopathology. Such methods can be used for insitu histopatholgy (e.g., within the body), and to obtain greatermolecular sensitivity. While computing has been used post-acquisitionfor improved analysis in pathology, computed molecular histopathologyoffers a paradigm shift in methodology that has the potential to changelong-standing practices in pathology. While compatible with currentanalytical approaches—whether manual or computerized (Gurcan et al.,Biomedical Engineering, IEEE Reviews 2:147-171, 2009), it also enablespathology to move closer to real-time, in situ assessment. Furtherdevelopments in computed histopathology have the potential to makeon-demand staining and assessments routine.

Methods of Stain-Free Imaging

Provided herein are methods of imaging a sample, such as a sample thathas not been stained or contacted with a dye or label, such as thosetypically used in histopathlogy, IHC, and ISH. In particular examples,the method includes obtaining a spectroscopic image (such as an infrared(IR) imaging dataset) of the sample, which contains a spectrum at everypixel. An image plotted using any combination of the information in thespectrum can be referred to as an IR image. The dimensionality of thespectroscopic image (e.g., IR image) can be reduced, thereby generatinga reduced spectroscopic image (e.g., reduced IR image). The reducedspectroscopic image is compared to one or more controls, for exampleusing a statistical pattern recognition approach. The spectra in thespectroscopic image (entire or reduced) are related to known stainingpatterns in the sample. Spectral features that allow prediction of thestaining patterns are found using statistical pattern recognitionapproaches, such as Bayesian, support vector machine or artificialneural network (e.g., see Webb and Copsey, Statistical PatternRecognition, 3^(rd) edition, 2011, Wiley, ISBN: 978-0-470-68227-2; Shenet al., Expert Rev Proteomics. 4(4):453-63, 2007). Spectral and spatialfeatures can be used to provide a color coded image that resembles astained image and its concordance with the control can be verified. Anoutput computed stain image (such as one that is comparable to (e.g.,provides the same information) one that would have been obtained if astain interest, such as H&E stain or BRCA1 protein staining, was used)is generated from the entire or the reduced spectroscopic image. Upontraining with appropriate control data/samples, the spectroscopic (e.g.,IR) features and pattern recognition approach can be used together topredict the staining patterns in any new (e.g., test) sample.

In some examples, a control set of samples is analyzed to obtain acontrol or control database for relevant samples, to which theexperimental or test sample is compared to. For example, a set ofcontrols of the target sample (e.g., lung tissue) stained with a stainor dye of interest (e.g., H&E stain, Congo Red stain, or antibodyspecific for a target protein, that is directly or indirectly labeled(e.g., with a labeled secondary antibody)), can be used to generate orbuild an algorithm which is subsequently applied to the test sample. Forexample, if the test sample is lung tissue and the target stain is CongoRed, then control samples of known lung tissue stained with Congo Redcan be analyzed as described herein (e.g., obtain spectroscopicimage/data, optionally reduce spectroscopic image/data, and input into anetwork for training) to generate the algorithm to which an experimentallung sample is compared to generate an output computed Congo Red-likeimage. Thus, a plurality of samples stained with a target stain (such asH&E, Bismark Brown, Nile Blue or antibody specific for a target protein,that is directly or indirectly labeled, e.g., with a fluorophore orenzyme), which are the same sample type of interest (e.g., breasttissue, lung tissue, water sample) can be used to train a network. Inone example, at least 10, at least 20, at least 50, at least 75, atleast 100, at least 200, at least 500, or at least 1000 samples (such as10-50, 10-100, 50-100 or 100-1000 samples) are used to train thenetwork. Thus, if training using control samples is needed, prior togenerating an output computed stain image for the test sample, themethods can include obtaining a spectroscopic image of thecontrol-stained sample, optionally reducing the spectroscopic image(e.g., use the entire spectra or the reduced spectra), then imputingthis information into a network, such as a neural network, supportvector machine, or Bayesian classifier. The network relates thebiochemical properties of the spectroscopic image/data to molecular ordye parameters, and transforms this to color values or stainingintensity on each pixel. For example, a spectroscopic imaging data setis obtained for each training sample, thereby generating spectra (e.g.,IR spectra) at every pixel. The dimensionality of the trainingspectroscopic imaging data set can be reduced, thereby generating areduced spectroscopic imaging data set, which is inputted into a networkfor training. The resulting trained samples result in an algorithm andparameters for the network, which can be used to compare a test sampleto, in order to generate an output image. The result of training is aset of parameters defining the neural network that can be applied to anyunknown samples, to produce an output computed stain image withoutstaining sample.

In some examples, the sample is analyzed ex situ, for example by using asample removed from a subject, such as a sample removed during asurgical procedure (e.g., frozen section). For example, the disclosedmethods can be used to ensure that clean margins are obtained afterremoving a tumor. In other examples, the sample is measured in vivo orin situ, for example while a patient is undergoing a medical procedure,such as a surgery to remove a tumor.

One or more steps of the method can be performed by a computer.

FIG. 4 illustrates a method for analyzing sample, for example as a meansto diagnose a disease. Although the operations of some of the disclosedmethods are described in a particular, sequential order for convenientpresentation, it should be understood that this manner of descriptionencompasses rearrangement, unless a particular ordering is required byspecific language set forth below. For example, operations describedsequentially may in some cases be rearranged or performed concurrently.Moreover, for the sake of simplicity, the attached figures may not showthe various ways in which the disclosed methods can be used inconjunction with other methods.

Any of the disclosed methods can be implemented as computer-executableinstructions stored on one or more computer-readable media (e.g.,non-transitory computer-readable media, such as one or more opticalmedia discs, volatile memory components (such as DRAM or SRAM), ornonvolatile memory components (such as hard drives)) and executed on acomputer (e.g., any commercially available computer, including smartphones or other mobile devices that include computing hardware). Any ofthe computer-executable instructions for implementing the disclosedtechniques as well as any data created and used during implementation ofthe disclosed methods can be stored on one or more computer-readablemedia (e.g., non-transitory computer-readable media). Thecomputer-executable instructions can be part of, for example, adedicated software application or a software application that isaccessed or downloaded via a web browser or other software application(such as a remote computing application). Such software can be executed,for example, on a single local computer (e.g., any suitable commerciallyavailable computer) or in a network environment (e.g., via the Internet,a wide-area network, a local-area network, a client-server network (suchas a cloud computing network), or other such network) using one or morenetwork computers.

For clarity, only certain selected aspects of the software-basedimplementations are described. Other details that are well known in theart are omitted. For example, it should be understood that the disclosedtechnology is not limited to any specific computer language or program.For instance, the disclosed technology can be implemented by softwarewritten in C++, Java, Perl, JavaScript, IDL, Matlab, Adobe Flash, or anyother suitable programming language. Likewise, the disclosed technologyis not limited to any particular computer or type of hardware. Certaindetails of suitable computers and hardware are well known and are thusnot be set forth in detail in this disclosure.

The disclosed methods, apparatus, and systems should not be construed aslimiting in any way. Instead, the present disclosure is directed towardall novel and nonobvious features and aspects of the various disclosedembodiments, alone and in various combinations and subcombinations withone another. The disclosed methods, apparatus, and systems are notlimited to any specific aspect or feature or combination thereof, nor dothe disclosed embodiments require that any one or more specificadvantages be present or problems be solved.

Turning to FIG. 4, in process block 110, a spectroscopic image (e.g., IRabsorbance data) of an unstained sample (such as one containing a tumoror portion thereof) are acquired. For example, FT-IR images of a tissuesample can be taken directly, or obtained from another source. Inprocess block 112, the dimensionality of the spectroscopic image or data(e.g., IR data) is reduced, for example using principal componentanalysis (PCA). The reduction process is used to reduce the amount ofdata used in subsequent steps, for example by eliminating regions in thesample that are not of interest. In process block 114, the reducedspectroscopic image or data is compared to a control or databaseanalyzed by an algorithm, wherein the database includes a plurality ofanalyzed samples that were stained with the target stain or dye. Inprocess block 116, an output computed stain image is generated. Forexample, the computed stain image can be comparable to an image obtainedwith the same tissue and stained with the target stain or dye.

Obtaining Spectroscopic Image

One skilled in the art will appreciate that the spectroscopic image canobtained using IR (such as mid-IR), Raman, fluorescence, lifetimeterahertz or any other form of spectroscopy. For example, IR microscopesand/or Fourier transform infrared spectrometers can be used. Spectra maybe obtained using a Fourier transform spectrometer, filters, lasers suchas quantum cascade lasers or using any such spectral resolution devicesuch as grating-based spectrometer. In one example, the spectroscopicimage of the sample is obtained using spectroscopic imaginginstrumentation, such as a mid-IR imaging system. IR light is light witha wavelength of about 750 nm to 1 mm. The mid-IR range is about 2 to 14microns. The optical frequencies of the mid-IR region of the spectrumare in the range of molecular vibrational frequencies. Hence, if amolecular species is present, light is absorbed in a pattern consistentwith its molecular constitution. The absorption spectrum can be used asa pattern of composition as well as metabolic activity. In one example,when obtaining the spectroscopic image, at least one mid-IR scan isobtained for each pixel in the image. For example, at least one spatialdimension (such as 1, 2 or 3 spatial dimensions) and at least onespectral (e.g., chemistry) dimension can be obtained, for example foreach pixel in the image. In some examples, a 2-dimensional IR image isobtained, in other examples a 3-dimensional image is obtained.

Data Transformation

The resulting spectroscopic image and data (e.g., mid-IR images andspectra) can be analyzed to reduce the dimensionality of the spectraldata obtained, for example using a computer program. The spectroscopicimage and data can be filtered in some method examples to remove datathat may be considered unreliable or unnecessary. It is understood thatthere are many methods known in the art for assessing such data. Methodsof identifying and eliminating non-useful information from a dataset areknown, and the disclosure is not limited to particular methods.Exemplary methods include pattern recognition algorithms such asprincipal component analysis (PCA) or a metrics approach. In someexamples, spectral data may be excluded from analysis, in some cases, ifit is not detected. For example, for each spectra and each pixelanalyzed, there are about 2000 datapoints. To reduce the amount of dataused in subsequent steps, regions not of interest in the sample can beeliminated, such as non-informative parts of the spectrum, pixelswithout information or without biological material, and the like. In oneexample, only pixels above a threshold absorption in the Amide I regionof the spectrum (1650cm⁻¹) are considered. Pixels with Amide Iabsorption below this threshold can be assumed empty and not consideredin the analysis.

PCA is a mathematical method that uses an orthogonal transformation toconvert a set of observations (here, a spectroscopic image and data,such as IR spectra) of possibly correlated variables into a set ofvalues of linearly uncorrelated variables called principal components.Principal components are orthogonal information contained in thespectra. Thus, the information contained in one is not reproduced inanother. Hence, they are a tool for reduction of data size. In themetrics approach, which is based on biochemical knowledge, humanknowledge is used to describe spectroscopic (e.g., IR) features. Asopposed to PCA method, which is automated, this method allows for use ofprior human experiences (see for example Bhargava et al., BiochimBiophys Acta. 1758:830-845, 2006).

In some examples, portions of spectroscopic images or data (e.g.,particular pixels or portions of the spectra) that exhibit no, or lowvariance may be excluded from further analysis. In one example,low-variance spectroscopic images or data are excluded from the analysisvia a Chi-Square test. A portion of a spectroscopic image or data can beconsidered to be low-variance if its transformed variance is to the leftof the 99 percent confidence interval of the Chi-Squared distributionwith (N-1) degrees of freedom. In one example, spectroscopic images ordata or a given stain or dye can be excluded from further analysis ifthey contain less than a minimum number of signals or a desired level ofexpression. In some examples, a statistical outlier program can be usedthat determines whether one of several replicates is statistically anoutlier compared to the others, such as judged by being “x” standarddeviations (SD) (e.g. at least 2-SD or at least 3-SD) away from theaverage, or CV% of replicates greater than a specified amount (e.g., atleast 8% in log-transformed space). For example, an outlier could resultfrom there being a problem with one of the pixels or due to an imagingartifact.

In some examples where spectroscopic images or data is measured insample replicates (e.g., triplicates), reproducibility can be measuredby pairwise correlation and by pairwise sample linear regression, and acorrelation r>=0.95 used as acceptance of replicate (e.g., triplicate)reproducibility. In more specific examples, replicates with pairwisecorrelation r=>0.90 can be further reviewed by a simple regressionmodel; in which case, if the intercept of the linear regression isstatistically significantly different from zero, the replicate removedfrom further consideration. Any sample with more than 25% (e.g., 1 outof 4) or more, 33% (e.g., 1 out of 3) or more, 50% (e.g., 2 out of 4) ormore, or 67% (e.g., 2 out of 3) or more failed replicates may beconsidered a “failed sample” and removed from further analysis.

Generating Computed Output Image

Once the spectroscopic image and data (e.g., mid-IR images and spectra)is reduced, it can be used to generate an output computed stain image(such as one that is comparable to the stain or target agent ofinterest, such as H&E stain or BRCA1 protein staining). The reducedspectroscopic image and data is input or applied to an algorithm thatrelates the detected spectral (e.g., IR) properties to dye parameters(such as a particular stain) or molecular parameters (such as aparticular pathogen, cell, organelle, protein, or nucleic acid), andtransforms this to color values or staining intensity on each pixel, orboth. Thus, the reduced spectroscopic image and data can be compared tothe parameters defining the network generated from control samples,which can then provide a color value for every pixel. The computedoutput image is simply the values plotted.

In some examples, the samples are also analyzed using light microscopy(brightfield image). In particular examples the method includesobtaining a brightfield optical image and a spectroscopic image fromsame unstained sample. The obtained data is combined with patternrecognition algorithms. The resulting refined data is input into anetwork that relates the biochemical properties to molecular or dyeparameters, and transforms this to color values or staining intensity oneach pixel. Predicted values of stain or dye are used to generate acomputed stain image that is comparable to the target stain.

Multiplexing

In some examples, the method includes imaging a single sample multipletimes, for example to obtain or generate a plurality of images using themethod. The disclosed methods permit a single sample to be analyzedmultiple times, as the methods do not result in substantial degradationor alteration of the sample. In contrast to traditional stains or dyes,which are usually not reversible or can result in degradation of thesample if it is treated with multiple stains or dyes, the currentmethods permit one to perform multiple analysis on a single sample, suchas generating at least 2, at least 3, at least 4, at least 5, at least6, at least 7, at least 8, at least 9, at least 10, at least 15, atleast 20, at least 30, at least 40, at least 50, at least 75, or atleast 100 or more different output computed stain images which arecomparable to images that would be obtained using traditional stains ordyes. For example, a plurality of computed stain images for at least twodifferent stains can be generated from a single sample, such as at least3, at least 4, at least 5, at least 6, at least 7, at least 8, at least9, at least 10, at least 15, at least 20, at least 30, at least 40, atleast 50, at least 75, or at least 100 different stains (such as thoseprovided herein). For example, a plurality of computed stain images forat least two different targets can be generated from a single sample,such as at least 3, at least 4, at least 5, at least 6, at least 7, atleast 8, at least 9, at least 10, at least 15, at least 20, at least 30,at least 40, at least 50, at least 75, or at least 100 differentproteins, different pathogens, different cells, or combinations thereof.In addition, a plurality of computed stain images for at least twodifferent stains and for at least two different targets can be generatedfrom a single sample, at least 5, at least 6, at least 7, at least 8, atleast 9, at least 10, at least 15, at least 20, at least 30, at least40, at least 50, at least 75, or at least 100 different stains and/ortargets.

Samples

A sample can be any material to be analyzed by microscopy, such asbiological samples and tissues, samples obtained from the environment orfood, as well as non-biological samples, such as polymers andnanomaterials. For example, a polarized microscopy image could beobtained from a polymer sample without the use of polarizer. A samplecan be biological or non-biological and can be obtained from a subject,an environment, a system, or a process. Thus, in some examples, themethod includes obtaining the sample. For example, the sample can beobtained from a subject known or suspected of having a microbeinfection, from a subject known or suspected of having a tumor (such ascancer), or from a source known or suspected of being contaminated bymicrobe.

In one example, the sample is excised or removed from the subject, andsubsequently analyzed ex vivo. In another example, the sample isanalyzed in vivo, for example while the subject is undergoing a medicalprocedure, such as surgery or a biopsy.

Biological samples obtained from a subject can include genomic DNA, RNA(including mRNA), protein, or combinations thereof. Examples include atissue or tumor biopsy, fine needle aspirate, bronchoalveolar lavage,pleural fluid, spinal fluid, saliva, sputum, surgical specimen, lymphnode fluid, ascites fluid, peripheral blood or fractions thereof (suchas serum or plasma), urine, saliva, buccal swab, and autopsy material.Techniques for acquisition of such samples are well known in the art.Serum or other blood fractions can be prepared in the conventionalmanner. Samples can also include fermentation fluid and tissue culturefluid.

In one example the sample is obtained from a subject, such as a humansubject. Such a sample can be any solid or fluid sample obtained from,excreted by or secreted by the subject, such as cells, cell lysates,peripheral blood (or a fraction thereof such as serum or plasma), urine,bile, ascites, saliva, cheek swabs, tissue biopsy (such as a tumorbiopsy or lymph node biopsy), surgical specimen, bone marrow,amniocentesis samples, fine needle aspirates, cervical samples (forexample a PAP smear, cells from exocervix, or endocervix), cerebrospinalfluid, aqueous or vitreous humor, a transudate, an exudate (for example,fluid obtained from an abscess or any other site of infection orinflammation), fluid obtained from a joint (for example, a normal jointor a joint affected by disease, such as a rheumatoid arthritis,osteoarthritis, gout or septic arthritis) and autopsy material.

In one example the sample is obtained from the environment, such as froma body of water, air, sediment, dust, soil, wood, plants or food or fromthe interior or surface of an animate or inanimate object in a naturalor a residential, commercial, industrial, medical, academic,agricultural, or other man-made environment (e.g., food processing,production, and consumption facilities and disposal environments), andcan be obtained from an industrial source, such as a farm, waste stream,or water source. Thus, samples can be those obtained from anyenvironment known or suspected to harbor bacteria, microorganisms, ormulticellular material generally.

In one example the sample is a food sample (such as a meat, fruit,dairy, or vegetable sample) or a sample obtained from a food-processingplant. For example, using the methods provided herein, adulterants infood products can be detected, such as a pathogen or toxin or otherharmful product.

In some examples, the sample is a collected fluid, scraping, filtrand,or culture. In one example, the sample is a cytology sample.

Once a sample has been obtained, the sample can be used directly,concentrated (for example by centrifugation or filtration), purified,liquefied, diluted in a fluid, fixed (e.g., using formalin or heat)and/or embedded in wax (such as formalin-fixed paraffin-embedded (FFPE)samples), or combinations thereof. In some examples, the sample is notmanipulated prior to its analysis, other than to apply it to amicroscope slide or other solid support. In particular examples, samplesare used directly (e.g., fresh or frozen). In one example, the sample isheat-fixed to a microscope slide or other solid support.

Samples may be fresh or processed post-collection (e.g., for archivingpurposes). In some examples, processed samples may be fixed (e.g.,formalin-fixed) and/or wax- (e.g., paraffin-)embedded. Fixatives formounted cell and tissue preparations are well known in the art andinclude, without limitation, 95% alcoholic Bouin's fixative; 95% alcoholfixative; B5 fixative, Bouin's fixative, formalin fixative, Karnovsky'sfixative (glutaraldehyde), Hartman's fixative, Hollande's fixative,Orth's solution (dichromate fixative), and Zenker's fixative (see, e.g.,Carson, Histotechology: A Self-Instructional Text, Chicago:ASCP Press,1997).

In some examples, the tissue sample (or a fraction thereof) is presenton a solid support. Solid supports useful in disclosed methods need onlybear the biological sample and, optionally, but advantageously, permitthe convenient detection of components (e.g., stroma, epithelial cells)in the sample. Exemplary supports include microscope slides (e.g., glassmicroscope slides or plastic microscope slides), specialized IRreflecting or transmitting materials (e.g., BaF₂ slides or reflectiveslides), coverslips (e.g., glass coverslips or plastic coverslips),tissue culture dishes, multi-well plates, membranes (e.g.,nitrocellulose or polyvinylidene fluoride (PVDF)) or BIACORE™ chips.

Control Samples and Obtaining Parameters for Network

The disclosed methods can include comparing the reduced spectroscopicimage from unstained tissue (the test sample) to one or more controlsamples stained with the target stain or dye. For example, the controlsamples can be a plurality of samples stained and analyzed previously.These trained samples provide an algorithm and parameters for a network,which can be used to compare the test sample to, in order to generate anoutput image. In some examples, the algorithm obtained from the trainedsamples is already in hand. In other examples, it is generated as partof the method, for example by using statistical learning algorithms.

In one example, the control samples include a plurality of samples ofthe same type as the test sample. For example if the test sample is abreast sample, the control samples can also be breast samples, and ifthe test sample is a breast cancer sample, the control samples can alsobe breast cancer samples. Thus, the control sample can include, forexample, normal tissue or cells, tissue or cells collected from apatient or patient population in which it is known that a benign tumorwas present, or tissue or cells collected from a patient or patientpopulation in which it is known that a particular cancer or infectionwas present. Similarly, the control sample can include an environmentalor food sample containing the pathogen or spores of interest.

The control samples can include a plurality of samples that are stainedwith the target stain (in contrast to the test sample, which is notstained). For example if the output computer stain image desired is onethat replicates an H&E image, the control samples are stained with H&E.For example if the output computer stain image desired is one thatreplicates Congo Red stained image, the control samples are stained withCongo Red. Similarly, if the output computer stain image desired is onethat shows a target protein or cell, that is it replicates an image thatresults from staining the sample with an antibody specific for thetarget protein or cell, the control samples can stained with theantibody specific for the target protein or cell (for example by usingan antibody that is directly or indirectly labeled).

If the algorithm and parameters for a network from the trained samplesis available, it can be used to analyze the test sample to generate anoutput computed stain image. If the algorithm and parameters for anetwork from the trained samples is not available it can be generatedfrom appropriately stained control samples, for example usingstatistical learning algorithms. The stained control samples areanalyzed to obtain a spectroscopic image, which is optionally reduced.This information is applied to a network, such as a neural network,support vector machine, or Bayesian classifier, to produce an algorithmand parameters for the network needed to generate an output computedstain image for the test sample.

In some methods, the reduced spectroscopic image from the test sample isapplied to an algorithm in order to generate the output computed stainimage that is comparable to the stain or target of interest. Aclassifier is a predictive model (e.g., algorithm or set of rules) thatcan be used to classify test samples or portions thereof (e.g., pixelsof a reduced spectroscopic image) into classes (or groups) (e.g., aparticular staining intensity or color) based on the spectral propertiesdetected in such samples. A classifier is trained on one or more sets ofsamples for which the desired class value(s) (e.g., staining patternand/or intensity) is (are) known. Once trained, the classifier is usedto assign class value(s) to future observations. Typical classificationalgorithms, include: Centroid Classifiers, k Nearest Neighbors (kNN),Bayesian Classification (e.g., Naïve Bayes and Bayesian Networks),Decision Trees, Neural Networks, Regression Models, Linear DiscriminantAnalysis, and Support Vector Machines.

Exemplary algorithms include, methods that handle large numbers ofvariables directly such as statistical methods and methods based onmachine learning techniques. Statistical methods include penalizedlogistic regression, prediction analysis of microarrays (PAM), methodsbased on shrunken centroids, support vector machine analysis, andregularized linear discriminant analysis. Machine learning techniquesinclude bagging procedures, boosting procedures, random forestalgorithms, and combinations thereof.

In some embodiments, results are classified using a trained algorithm.Trained algorithms include algorithms that have been developed using areference set of samples of the same type as the target sample (e.g.,lung tissue) stained with a stain or dye of interest (e.g., H&E stain,Congo Red stain, or antibody specific for a target protein, that isdirectly or indirectly labeled (e.g., with a labeled secondaryantibody)). Algorithms suitable for categorization of samples include,but are not limited to, k-nearest neighbor algorithms, concept vectoralgorithms, naive bayesian algorithms, neural network algorithms, hiddenmarkov model algorithms, genetic algorithms, and mutual informationfeature selection algorithms or any combination thereof. In some cases,trained algorithms incorporate data other than staining data such as butnot limited to diagnosis by cytologists or pathologists, informationprovided by a disclosed pre-classifier algorithm or gene set, orinformation about the medical history of a subject from whom a testedsample is taken.

In some specific embodiments, a support vector machine (SVM) algorithm,a random forest algorithm, or a combination thereof providesclassification of samples or portions thereof (such as pixels) whichpermit generation of a computed output image. In some cases, aclassifier algorithm may be supplemented with a meta-analysis approachsuch as that described by Fishel et al. (Bioinformatics, 23:1599(2007)). In some cases, the classifier algorithm may be supplementedwith a meta-analysis approach such as a repeatability analysis.

In some methods, the experimental values determined from the test sampleare compared to a standard value or a control sample, such as aprobability distribution function (pdf) value (or range of values) forreference or control samples. A standard value or range of can include,without limitation, the pdf value or range of values for metrics (suchas spectral peak heights, ratios of peaks, peak areas and centers ofgravity of the IR image, for example, a peak ratio of positions 1080:1456 cm⁻¹, 1556:1652 cm⁻¹, 1080: 1238 cm⁻¹, and 1338: 1080 cm⁻¹, acenter of gravity of position 1216-1274 cm⁻¹, and a peak area ofposition 1426-1482 cm⁻¹) for stroma and for epithelium. A standard valueor range of can include, without limitation, the pdf value or range ofvalues for the spatial pattern of epithelium pixels for a target cell,protein, or stain. Such values can be obtained from a patient or patientpopulation or other controls as described above.

Detection of Cellular Structures

In particular examples, at least one target to be detected by thedisclosed methods is a cellular structure. Such structures are typicallyidentified and localized in a sample using stains, such as H&E, iodine,methylene blue, Congo Red, carmine, toluidene blue, Sudan III, Wright'sstain, acridine orange, Bismarck brown, acid fuchsine, and others listedherein. Exemplary non-limiting cellular structures that can be detectedusing the disclosed methods include carbohydrates, nucleus, nuclei,lumen, chromosomes, connective tissue, cytoskeleton, lipids, mucins,glycogen, and mitochondria, and collagen.

Detection of Cells

In particular examples, at least one target to be detected by thedisclosed methods is a particular cell type. Such cells are typicallyidentified and localized in a sample using stains or labeled antibodies.For example, target cells can be identified due to their expression ofparticular proteins, such as on the cell surface. Exemplary non-limitingcells that can be detected using the disclosed methods include tumorcells (for example by detecting tumor associated antigens as discussedbelow), epithelium, connective tissue, cardiac muscle cells, skeletalmuscle cells, smooth muscle cells, neural cells, epidermal cells, stemcells, cells from particular organs such as a lung cells, pancreaticcells, thyroid cells, liver cells, plant cells, and the like.

Detection of Pathogens/Microbes

In particular examples, at least one target to be detected by thedisclosed methods is a pathogen/microbe. Such pathogens are typicallyidentified and localized in a sample using labeled antibodies, whereinthe antibody is specific for a protein on the microbe. The disclosedmethods can be used to determine if a sample contains one or moremicrobes, for example to determine if a subject is infected with aparticular microbe, such as a bacterium, virus, fungi or protozoa.

Any pathogen or microbe can be detected using the methods providedherein. For example, microbes, as well as bacterial spores, can bedetected. In some examples, a particular microbial cell is detected, ora particular virus. In some examples, intact microbes are detected, forexample by detecting the cell wall composition of bacteria or the capsidcomposition of a virus.

Exemplary pathogens include, but are not limited to, viruses, bacteria,fungi, nematodes, and protozoa. A non-limiting list of pathogens thatcan be detected using the disclosed methods are provided below.

For example, viruses include positive-strand RNA viruses andnegative-strand RNA viruses. Exemplary positive-strand RNA virusesinclude, but are not limited to: Picornaviruses (such as Aphthoviridae[for example foot-and-mouth-disease virus (FMDV)]), Cardioviridae;Enteroviridae (such as Coxsackie viruses, Echoviruses, Enteroviruses,and Polioviruses); Rhinoviridae (Rhinoviruses)); Hepataviridae(Hepatitis A viruses); Togaviruses (examples of which include rubella;alphaviruses (such as Western equine encephalitis virus, Eastern equineencephalitis virus, and Venezuelan equine encephalitis virus));Flaviviruses (examples of which include Dengue virus, West Nile virus,and Japanese encephalitis virus); Calciviridae (which includes Norovirusand Sapovirus); and Coronaviruses (examples of which include SARScoronaviruses, such as the Urbani strain).

Exemplary negative-strand RNA viruses include, but are not limited to:Orthomyxyoviruses (such as the influenza virus), Rhabdoviruses (such asRabies virus), and Paramyxoviruses (examples of which include measlesvirus, respiratory syncytial virus, and parainfluenza viruses).

Viruses also include DNA viruses. DNA viruses include, but are notlimited to: Herpesviruses (such as Varicella-zoster virus, for examplethe Oka strain; cytomegalovirus; and Herpes simplex virus (HSV) types 1and 2), Adenoviruses (such as Adenovirus type 1 and Adenovirus type 41),Poxviruses (such as Vaccinia virus), and Parvoviruses (such asParvovirus B19).

Another group of viruses includes Retroviruses. Examples of retrovirusesinclude, but are not limited to: human immunodeficiency virus type 1(HIV-1), such as subtype C; HIV-2; equine infectious anemia virus;feline immunodeficiency virus (FIV); feline leukemia viruses (FeLV);simian immunodeficiency virus (SIV); and avian sarcoma virus.

In one example, the virus detected with the disclosed methods is one ormore of the following: HIV (for example an HIV antibody, p24 antigen, orHIV genome); Hepatitis A virus (for example an Hepatitis A antibody, orHepatitis A viral genome); Hepatitis B (HB) virus (for example an HBcore antibody, HB surface antibody, HB surface antigen, or HB viralgenome); Hepatitis C (HC) virus (for example an HC antibody, or HC viralgenome); Hepatitis D (HD) virus (for example an HD antibody, or HD viralgenome); Hepatitis E virus (for example a Hepatitis E antibody, or HEviral genome); a respiratory virus (such as influenza A & B, respiratorysyncytial virus, human parainfluenza virus, or human metapneumovirus),or West Nile Virus.

Pathogens also include bacteria. Bacteria can be classified asgram-negative or gram-positive. Examples of bacteria that can bedetected with the disclosed methods, include without limitation:Acinetobacter baumanii, Actinobacillus sp., Actinomycetes, Actinomycessp. (such as Actinomyces israelii and Actinomyces naeslundii), Aeromonassp. (such as Aeromonas hydrophila, Aeromonas veronii biovar sobria(Aeromonas sobria), and Aeromonas caviae), Anaplasma phagocytophilum,Alcaligenes xylosoxidans, Acinetobacter baumanii, Actinobacillusactinomycetemcomitans, Bacillus sp. (such as Bacillus anthracis,Bacillus cereus, Bacillus subtilis, Bacillus thuringiensis, and Bacillusstearothermophilus), Bacteroides sp. (such as Bacteroides fragilis),Bartonella sp. (such as Bartonella bacilliformis and Bartonellahenselae, Bifidobacterium sp., Bordetella sp. (such as Bordetellapertussis, Bordetella parapertussis, and Bordetella bronchiseptica),Borrelia sp. (such as Borrelia recurrentis, and Borrelia burgdorferi),Brucella sp. (such as Brucella abortus, Brucella canis, Brucellamelintensis and Brucella suis), Burkholderia sp. (such as Burkholderiapseudomallei and Burkholderia cepacia), Campylobacter sp. (such asCampylobacter jejuni, Campylobacter coli, Campylobacter lari andCampylobacter fetus), Capnocytophaga sp., Cardiobacterium hominis,Chlamydia trachomatis, Chlamydophila pneumoniae, Chlamydophila psittaci,Citrobacter sp. Coxiella burnetii, Corynebacterium sp. (such as,Corynebacterium diphtheriae, Corynebacterium jeikeum andCorynebacterium), Clostridium sp. (such as Clostridium perfringens,Clostridium difficile, Clostridium botulinum and Clostridium tetani),Eikenella corrodens, Enterobacter sp. (such as Enterobacter aerogenes,Enterobacter agglomerans, Enterobacter cloacae and Escherichia coli,including opportunistic Escherichia coli, such as enterotoxigenic E.coli, enteroinvasive E. coli, enteropathogenic E. coli,enterohemorrhagic E. coli, enteroaggregative E. coli and uropathogenicE. coli) Enterococcus sp. (such as Enterococcus faecalis andEnterococcus faecium) Ehrlichia sp. (such as Ehrlichia chafeensia andEhrlichia canis), Erysipelothrix rhusiopathiae, Eubacterium sp.,Francisella tularensis, Fusobacterium nucleatum, Gardnerella vaginalis,Gemella morbillorum, Haemophilus sp. (such as Haemophilus influenzae,Haemophilus ducreyi, Haemophilus aegyptius, Haemophilus parainfluenzae,Haemophilus haemolyticus and Haemophilus parahaemolyticus, Helicobactersp. (such as Helicobacter pylori, Helicobacter cinaedi and Helicobacterfennelliae), Kingella kingii, Klebsiella sp. (such as Klebsiellapneumoniae, Klebsiella granulomatis and Klebsiella oxytoca),Lactobacillus sp., Listeria monocytogenes, Leptospira interrogans,Legionella pneumophila, Leptospira interrogans, Peptostreptococcus sp.,Moraxella catarrhalis, Morganella sp., Mobiluncus sp., Micrococcus sp.,Mycobacterium sp. (such as Mycobacterium leprae, Mycobacteriumtuberculosis, Mycobacterium intracellulare, Mycobacterium avium,Mycobacterium bovis, and Mycobacterium marinum), Mycoplasm sp. (such asMycoplasma pneumoniae, Mycoplasma hominis, and Mycoplasma genitalium),Nocardia sp. (such as Nocardia asteroides, Nocardia cyriacigeorgica andNocardia brasiliensis), Neisseria sp. (such as Neisseria gonorrhoeae andNeisseria meningitidis), Pasteurella multocida, Plesiomonasshigelloides. Prevotella sp., Porphyromonas sp., Prevotellamelaninogenica, Proteus sp. (such as Proteus vulgaris and Proteusmirabilis), Providencia sp. (such as Providencia alcalifaciens,Providencia rettgeri and Providencia stuartii), Pseudomonas aeruginosa,Propionibacterium acnes, Rhodococcus equi, Rickettsia sp. (such asRickettsia rickettsii, Rickettsia akari and Rickettsia prowazekii,Orientia tsutsugamushi (formerly: Rickettsia tsutsugamushi) andRickettsia typhi), Rhodococcus sp., Serratia marcescens,Stenotrophomonas maltophilia, Salmonella sp. (such as Salmonellaenterica, Salmonella typhi, Salmonella paratyphi, Salmonellaenteritidis, Salmonella cholerasuis and Salmonella typhimurium),Serratia sp. (such as Serratia marcesans and Serratia liquifaciens),Shigella sp. (such as Shigella dysenteriae, Shigella flexneri, Shigellaboydii and Shigella sonnei), Staphylococcus sp. (such as Staphylococcusaureus, Staphylococcus epidermidis, Staphylococcus hemolyticus,Staphylococcus saprophyticus), Streptococcus sp. (such as Streptococcuspneumoniae (for example chloramphenicol-resistant serotype 4Streptococcus pneumoniae, spectinomycin-resistant serotype 6BStreptococcus pneumoniae, streptomycin-resistant serotype 9VStreptococcus pneumoniae, erythromycin-resistant serotype 14Streptococcus pneumoniae, optochin-resistant serotype 14 Streptococcuspneumoniae, rifampicin-resistant serotype 18C Streptococcus pneumoniae,tetracycline-resistant serotype 19F Streptococcus pneumoniae,penicillin-resistant serotype 19F Streptococcus pneumoniae, andtrimethoprim-resistant serotype 23F Streptococcus pneumoniae,chloramphenicol-resistant serotype 4 Streptococcus pneumoniae,spectinomycin-resistant serotype 6B Streptococcus pneumoniae,streptomycin-resistant serotype 9V Streptococcus pneumoniae,optochin-resistant serotype 14 Streptococcus pneumoniae,rifampicin-resistant serotype 18C Streptococcus pneumoniae,penicillin-resistant serotype 19F Streptococcus pneumoniae, ortrimethoprim-resistant serotype 23F Streptococcus pneumoniae),Streptococcus agalactiae, Streptococcus mutans, Streptococcus pyogenes,Group A streptococci, Streptococcus pyogenes, Group B streptococci,Streptococcus agalactiae, Group C streptococci, Streptococcus anginosus,Streptococcus equismilis, Group D streptococci, Streptococcus bovis,Group F streptococci, and Streptococcus anginosus Group G streptococci),Spirillum minus, Streptobacillus moniliformi, Treponema sp. (such asTreponema carateum, Treponema petenue, Treponema pallidum and Treponemaendemicum, Tropheryma whippelii, Ureaplasma urealyticum, Veillonellasp., Vibrio sp. (such as Vibrio cholerae, Vibrio parahemolyticus, Vibriovulnificus, Vibrio parahaemolyticus, Vibrio vulnificus, Vibrioalginolyticus, Vibrio mimicus, Vibrio hollisae, Vibrio fluvialis, Vibriometchnikovii, Vibrio damsela and Vibrio fumisii), Yersinia sp. (such asYersinia enterocolitica, Yersinia pestis, and Yersiniapseudotuberculosis) and Xanthomonas maltophilia among others.

Protozoa, nemotodes, and fungi are also types of pathogens. Exemplaryprotozoa include, but are not limited to, Plasmodium (e.g., Plasmodiumfalciparum to diagnose malaria), Leishmania, Acanthamoeba, Giardia,Entamoeba, Cryptosporidium, Isospora, Balantidium, Trichomonas,Trypanosoma (e.g., Trypanosoma brucei), Naegleria, and Toxoplasma.Exemplary fungi include, but are not limited to, Coccidiodes immitis andBlastomyces dermatitidis.

In one example, bacterial spores are detected. For example, the genus ofBacillus and Clostridium bacteria produce spores that can be detected.Thus, C. botulinum, C. perfringens, B. cereus, and B. anthracis sporescan be detected (for example detecting anthrax spores). One will alsorecognize that spores from green plants can also be detected using themethods provided herein.

Detection of Tumors

In particular examples, at least one target to be detected by thedisclosed methods is a protein, such as a unique cell identified,functional protein expression or tumor-associated antigen. Such proteinsare typically identified and localized in a sample using labeledantibodies. Tumor-associated antigens are antigens differentiallyexpressed by tumor cells, typically in significantly greater amounts,than usually found on a normal cell or tissue. Elevated levels of theseantigens can be used as tumor markers, for example for diagnosis of aparticular cancer.

Thus, in one example, the methods permit detection of one or moretumor-associated or tumor-specific antigens, without the use of stainsor antibodies (such as directly or indirectly labeled antibodies).Exemplary tumor-associated antigens include but are not limited toCA-125 (ovarian cancer marker), alphafetoprotein (AFP, liver cancer);immature laminin receptor and 9D7 (renal cell carcinoma)carcinoembryonic antigen (CEA) and SAP-1 (colorectal cancer); BRCA1 and2 (breast and ovarian cancer), TAG-72 (prostate cancer); complementfactor H related protein (CFHrp; bladder cancer); mesothelin (pancreaticcancer); melanoma-associated antigen (MAGE), and the like. BING-4(melanoma); calcium-activated chloride channel 2 (lung carcinoma);cyclin-B1 (many cancers); Ep-CAM (breast carcinoma); EphA3 (manycancers); telomerase (many cancers); p53 (many cancers); Ras (manycancers); BRAF (many cancers); and survivin (many cancers).

In another example, the tumor antigen is an oncoviral antigen, such asHPV E6 and E7 (cervical cancer).

Also see, Tumor-Associated Antigens. Edited by Olivier Gires and BarbaraSeliger, Part One Tumor-associated Antigens (TAAs): Subclasses of TAAs.Copyright 2009, herein incorporated by reference.

Outputs

In some embodiments, once a sample is analyzed, an indication of thatanalysis can be displayed and/or conveyed to a clinician or othercaregiver. For example, the results of the test can be provided to auser (such as a clinician or other health care worker, laboratorypersonnel, or patient) in a perceivable output that provides informationabout the results of the test. In some embodiments, the output is apaper output (for example, a written or printed output), a display on ascreen, a graphical output (for example, a graph, chart, voltammetrictrace, or other diagram), or an audible output. For example, thecomputed stain image can be outputted, for example in a visual form,such as in color.

In other embodiments, the output is a diagnosis, such as whether thesample analyzed is cancerous or benign, whether the sample contains thetarget cell, protein or nucleic acid molecule (such as increasedexpression of such targets), or whether the sample is infected with thepathogen of interest. In additional embodiments, the output is agraphical representation, for example, a graph that indicates the value(such as amount or relative amount) of the likelihood that the sample iscancerous or benign, likelihood that the sample contains the targetprotein or nucleic acid molecule (such as the likelihood of increased ordecreased expression of such targets), or likelihood that the samplecontains the target pathogen. In some examples, the output is a numberon a screen/digital display, such as one indicating the probability ofthe sample being cancer or benign, probability of the sample containingthe target protein or nucleic acid molecule (such as the probability ofincreased or decreased expression of such targets), or probability thatthe sample contains the target pathogen. In some examples, the output istext, indicating the likelihood that the sample is cancerous or benign,likelihood that the sample contains the target protein or nucleic acidmolecule (such as the likelihood of increased or decreased expression ofsuch targets), or likelihood that the sample contains the targetpathogen, for example along with the corresponding implications to thepatient if appropriate. Sensitivity, specificity, and confidenceintervals may also be a part of the output. These outputs can be in theform of graphs or tabulated numbers. The output can be a color-codedimage with different colors indicating different probabilities of beingcancer or normal, probabilities that the sample contains the targetprotein or nucleic acid molecule (such as the likelihood of increased ordecreased expression of such targets), or probabilities that the samplecontains the target pathogen. In some embodiments, the output iscommunicated to the user, for example by providing an output viaphysical, audible, or electronic means (for example by mail, telephone,facsimile transmission, email, or communication to an electronic medicalrecord).

In some embodiments, the output is accompanied by guidelines forinterpreting the data, for example, numerical or other limits thatindicate whether the test sample is cancerous or benign, whether thesample contains the target protein or nucleic acid molecule (such aswhether there is increased or decreased expression of such targets), orwhether the sample contains the target pathogen. The guidelines need notspecify whether the test sample is cancerous or benign, whether thesample contains the target protein or nucleic acid molecule (such aswhether there is increased or decreased expression of such targets), orwhether the sample contains the target pathogen although it may includesuch a diagnosis. The indicia in the output can, for example, includenormal or abnormal ranges or a cutoff, which the recipient of the outputmay then use to interpret the results, for example, to arrive at adiagnosis, prognosis, or treatment plan. In other embodiments, theoutput can provide a recommended therapeutic regimen. In someembodiments, the test may include determination of other clinicalinformation (such as determining the amount of one or more additionalbiomarkers in the biological sample).

In particular examples, the methods provided herein have a sensitivityof at least 90%, at least 95%, at least 98%, at least 97%, or at least99% sensitivity, wherein sensitivity is the probability that astatistical test will be positive for a true statistic. In particularexamples, the methods provided herein have a specificity of at least70%, at least 75%, at least 80%, at least 82%, at least 85% or at least90% specificity, wherein specificity is the probability that astatistical test will be negative for a negative statistic.

Methods of Treatment

The disclosed methods can further include identifying and/or selectingsubjects for treatment based on the results of imaging the unstainedsample. Methods and therapeutic dosages of therapeutic agents are knownto those skilled in the art, and can be determined by a skilledclinician.

For example, if the analysis indicates that the sample is positive forcancer cells or proteins, the subject can be treated with (e.g.,administered) a therapeutically effective amount of an appropriatechemotherapy and/or biotherapy (e.g., antibodies specific for thetumor). Chemotherapies and biotherapies include therapeutic agents thatwhen administered in therapeutically effective amounts induce thedesired response (e.g., treatment of a cancer, for example by reducingthe size or volume of the tumor, or reducing the size, volume or numberof metastases). Examples of chemotherapies and bio-therapies include butare not limited to anti-neoplastic chemotherapeutic agents, antibiotics,alkylating agents and antioxidants, kinase inhibitors, and other agentssuch as antibodies. Particular examples of chemotherapeutic andbiotherapeutic agents that can be used include alkylating agents, suchas nitrogen mustards (for example, chlorambucil, chlormethine,cyclophosphamide, ifosfamide, and melphalan), nitrosoureas (for example,carmustine, fotemustine, lomustine, and streptozocin), platinumcompounds (for example, carboplatin, cisplatin, oxaliplatin, andBBR3464), busulfan, dacarbazine, mechlorethamine, procarbazine,temozolomide, thiotepa, and uramustine; folic acid (for example,methotrexate, pemetrexed, and raltitrexed), purine (for example,cladribine, clofarabine, fludarabine, mercaptopurine, and tioguanine),pyrimidine (for example, capecitabine), cytarabine, fluorouracil, andgemcitabine; plant alkaloids, such as podophyllum (for example,etoposide, and teniposide); microtubule binding agents (such aspaclitaxel, docetaxel, vinblastine, vindesine, vinorelbine (navelbine)vincristine, the epothilones, colchicine, dolastatin 15, nocodazole,podophyllotoxin, rhizoxin, and derivatives and analogs thereof), DNAintercalators or cross-linkers (such as cisplatin, carboplatin,oxaliplatin, mitomycins, such as mitomycin C, bleomycin, chlorambucil,cyclophosphamide, and derivatives and analogs thereof), DNA synthesisinhibitors (such as methotrexate, 5-fluoro-5′-deoxyuridine,5-fluorouracil and analogs thereof); anthracycline family members (forexample, daunorubicin, doxorubicin, epirubicin, idarubicin,mitoxantrone, and valrubicin); antimetabolites, such ascytotoxic/antitumor antibiotics, bleomycin, rifampicin, hydroxyurea, andmitomycin; topoisomerase inhibitors, such as topotecan and irinotecan;monoclonal antibodies, such as alemtuzumab, bevacizumab, cetuximab,gemtuzumab, rituximab, panitumumab, pertuzumab, and trastuzumab;photosensitizers, such as aminolevulinic acid, methyl aminolevulinate,porfimer sodium, and verteporfin, enzymes, enzyme inhibitors (such ascamptothecin, etoposide, formestane, trichostatin and derivatives andanalogs thereof), kinase inhibitors (such as imatinib, gefitinib, anderolitinib), gene regulators (such as raloxifene, 5-azacytidine,5-aza-2′-deoxycytidine, tamoxifen, 4-hydroxytamoxifen, mifepristone andderivatives and analogs thereof); and other agents, such asalitretinoin, altretamine, amsacrine, anagrelide, arsenic trioxide,asparaginase, axitinib, bexarotene, bevacizumab, bortezomib, celecoxib,denileukin diftitox, estramustine, hydroxycarbamide, lapatinib,pazopanib, pentostatin, masoprocol, mitotane, pegaspargase, tamoxifen,sorafenib, sunitinib, vemurafinib, vandetanib, and tretinoin. Othertherapeutic agents, for example anti-tumor agents, that may or may notfall under one or more of the classifications above, also are suitablefor administration in combination with the described specific bindingagents.

In another example, if the analysis indicates that the sample ispositive for a particular microbe, then the subject can be treated witha therapeutically effective amount of an appropriate antimicrobial, suchas one or more antiviral agents (e.g., oseltamivir, acyclovir,valaciclovir, abacavir, etc.), antibacterial agents (e.g., penicillin,amoxicillin, gentamicin, ciprofloxacin, levofloxacin, etc.), anti-fungalagents (e.g., benzimidazole fungicides, conazole fungicides, imidazolefungicides etc.), antiparasitic agents (e.g., niclosamide, tiabendazole,rifampin, melarsoprol, tinidazole, etc.), and the like.

Therapeutic agents can be administered to a subject in need of treatmentusing any suitable means known in the art. Methods of administrationinclude, but are not limited to, intradermal, transdermal,intramuscular, intraperitoneal, parenteral, intravenous, subcutaneous,vaginal, rectal, intranasal, inhalation, oral, or by gene gun.Intranasal administration refers to delivery of the compositions intothe nose and nasal passages through one or both of the nares and caninclude delivery by a spraying.

Computer-Readable Media

Also provided herein are computer-readable storage medium havinginstructions thereon for performing a method of analyzing a sample, forexample to diagnose the sample as a particular cancer type or as beinginfected with a particular pathogen. Thus, computer-readable storagemedium having instructions thereon for performing the methods describedherein are disclosed.

Any of the computer-readable media herein can be non-transitory (e.g.,memory, magnetic storage, optical storage, or the like).

Any of the storing actions described herein can be implemented bystoring in one or more computer-readable media (e.g., computer-readablestorage media or other tangible media).

Any of the things described as stored can be stored in one or morecomputer-readable media (e.g., computer-readable storage media or othertangible media).

Any of the methods described herein can be implemented bycomputer-executable instructions in (e.g., encoded on) one or morecomputer-readable media (e.g., computer-readable storage media or othertangible media). Such instructions can cause a computer to perform themethod. The technologies described herein can be implemented in avariety of programming languages.

Any of the methods described herein can be implemented bycomputer-executable instructions stored in one or more computer-readablestorage devices (e.g., memory, magnetic storage, optical storage, or thelike). Such instructions can cause a computer to perform the method.

Systems for Stain-Free Imaging

The present disclosure provides systems for imaging an unstained sample.for example, such a system can include a means for obtaining aspectroscopic image of the unstained sample, implemented rules forreducing dimensionality of the spectroscopic image, implemented rulesfor comparing the reduced spectroscopic image to a control image, andmeans for implementing the rules, thereby generating an output computedstain image from the reduced spectroscopic image and imaging theunstained sample. In some examples the rules include computer-executableinstructions stored on one or more computer-readable media (e.g.,non-transitory computer-readable media, such as one or more opticalmedia discs, volatile memory components (such as DRAM or SRAM), ornonvolatile memory components (such as hard drives)) and executed on acomputer (e.g., any commercially available computer, including smartphones or other mobile devices or tablets that include computinghardware).

Exemplary means for obtaining a spectroscopic image of the unstainedsample are known in the art, and the disclosure is not limited toparticular imaging systems. In one example, the means for obtaining aspectroscopic image of the unstained sample is a means for spectroscopy,for example IR (such as mid-IR), Raman, fluorescence, lifetime terahertzor any other form of spectroscopy. For example, spectra may be obtainedusing a Fourier transform spectrometer, filters, lasers such as quantumcascade lasers or using any such spectral resolution device such asgrating-based spectrometer. In one example, the spectroscopic image ofthe sample is obtained using spectroscopic imaging instrumentation, suchas IR spectroscopic imaging instrumentation (e.g., a mid-IR imagingsystem) or a Fourier Transform IR imaging system, such as one thatincludes an IR or Fourier transform infrared spectrometer. For example,IR microscopes and/or Fourier transform infrared spectrometers or thoseusing lasers or gratings for spectroscopy can be used for the means.

In some examples, the system also includes means to process the obtainedspectral data from the sample, such as a computer and software to removebackground absorbance and to baseline correct each pixel. In someexamples, the system further includes means to computationally reducenoise, to deconvolve the image, or both (such as an appropriatelyprogrammed computer).

The system can include implemented rules for reducing dimensionality ofthe spectroscopic image. Exemplary rules for reducing dimensionality ofthe spectroscopic image are known in the art, and the disclosure is notlimited to particular rules. Such rules can filter in some examples toremove data from the spectroscopic image generated that may beconsidered unreliable or unnecessary. In some examples, spectral datamay be excluded from analysis, in some cases, if it is not detected. Forexample, the rule can eliminate regions not of interest in the sample,such as non-informative parts of the spectrum, pixels withoutinformation or without biological material, and the like. In oneexample, only pixels above a threshold absorption in the Amide I regionof the spectrum (1650 cm⁻¹) are considered. Pixels with Amide Iabsorption below this threshold can be assumed empty by the rule and notconsidered in the analysis. Exemplary rules that can be used to reducethe dimensionality of the spectroscopic image include patternrecognition algorithms, such as principal component analysis (PCA) or ametrics approach.

The system can include implemented rules for comparing the reducedspectroscopic image to a control image (or plurality of images), such asthose that may be stored in a database. Exemplary rules for comparingthe reduced spectroscopic image to a control image are known in the art,and the disclosure is not limited to particular rules. Such rules canrelate the detected spectral (e.g., IR) properties to dye parameters(such as a particular stain) or molecular parameters (such as aparticular pathogen, cell, organelle, protein, or nucleic acid), andtransform this to color values or staining intensity on each pixel, orboth. For example, the rule can compare the parameters defining thenetwork generated from control samples (such as those stored in adatabase), which can then provide a color value for every pixel. Theabsorbance values of specific spectral bands may also be directlyrelated to the concentration of a dye or probe. Algorithms forcomparisons are routine in the art (e.g., statistical recognitionmethods)

The system can include means for implementing the rules. For example,the means can generate an output computed state image by plotting thevalues. Thus, an output computed stain image is generated from thereduced spectroscopic image and imaging the unstained sample. Suchexemplary means include a computer or network.

In some examples, the system further includes a means for obtaining abrightfield image of the unstained sample, such as a light microscopemeans. In some examples, the system further includes means for inputtingdata (such as a reduced spectroscopic image) into a network that relatesthe biochemical properties to molecular or dye parameters, and rulesthat transform this to color values or staining intensity on each pixel.Examples of such means include keyboards and software. Predicted valuesof stain or dye are used to generate a computed stain image that iscomparable to the target stain.

EXAMPLE 1 Materials and Methods

This example provides the materials and methods used in Examples 2-4below. Although the examples describe studies using breast tissue, oneskilled in the art will appreciate that other tissues and samples can beused based on the teachings provided herein.

The problem of spectral unmixing in breast tissue was solved herein bymapping the infrared images to bright-field images representing an arrayof common histological stains. Several tissue samples representing abroad panel of normal tissue, non-malignant and cancer subtypes,including tissue microarrays and surgical resections of breast biopsies,were used. A training set was constructed by first imaging the tissueusing mid-infrared spectroscopy. Adjacent sections were then stainedusing a panel of standard and immunohistochemical stains and imagedusing bright-field microscopy. The bright-field images were thenadjusted to overlay the spectroscopic images. This created a spatial(pixel-to-pixel) map to the IR data, from corresponding spectrum/RGBpairs were can extracted. A principal component analysis (PCA) was usedto reduce the dimensionality of the input spectra and train afeed-forward neural network using the PCA-projected spectra as input andthe associated bright-field color data as output. The high data rateprovided by IR spectroscopic imaging combined with the large number oftissue samples used provided millions of spectrum-RGB pairs for eachstain.

Tissue Microarrays

Tissue microarrays (TMAs) representing normal breast tissue, pre-cancer,non-malignant, and malignant breast cancer were acquired from thecooperative human tissue network via the Tissue array research program,NCI and from US Biomax (Rockville, Md.). TMAs are useful for analyzinglarge patient sets and to encounter a wide variety of cell types anddisease states to ensure robust classifiers (Camp et al., Lab Invest80:1943-9, 2000). The training and validation sample data sets consistedof 1.5 mm diameter needle core biopsies of at least 100 samples each.One section was placed on an IR transparent substrate, barium fluoride(BaF₂), for IR spectroscopic imaging while serial sections were placedon glass slides and stained with H&E, Masson's trichrome, and a panel ofIHC stains commonly used for breast cancer. This system is used forroutine clinical samples.

The tissues on BaF₂ had their paraffin removed by emersion in hexane at40° C. for 48 hrs. IR imaging was performed in transmission-mode using aPerkin Elmer Spotlight 400 Fourier Transform IR Imaging system intransmission mode at 6.25 μm spatial resolution and 4 cm⁻¹ spectralresolution from 750 cm⁻¹ to 4000 cm⁻¹. Spectral data was processed toremove background absorbance and each pixel was independently baselinecorrected.

Stained sections were imaged using a Hamamatsu NanoZoomer 2.0 seriesscanner with 20× objective (0.46 m/pixel). Bright-field images werefiltered and downsampled to match the resolution of the IR image andindividual cores were manually aligned to construct the training set.Since the infrared and bright-field images were from adjacent sections,cell-level alignment was not possible. To compensate for shiftedpositions in high-frequency features, a Gaussian blur with 5-pixelstandard deviation was applied to the bright-field image.

Surgical Resections

Formalin-fixed paraffin-embedded (FFPE) surgical specimens weresectioned and placed on MirrIR low-e microscope slides (KevleyTechnologies) and imaged using a Perkin Elmer Spotlight 400 FourierTransform IR Imaging system in reflection mode at 6.25 μm spatialresolution and 8 cm¹ spectral resolution from 750 cm⁻¹ to 4000 cm⁻¹.Spectral data was processed to remove background absorbance and eachpixel was independently baseline corrected. The sections were thenstained with H&E or Masson's trichrome. Stained sections were thenimaged using a Zeiss Axiovert 200M Microscope with a 2.5× Plan-Neofluarobjective and Zeiss AxioCam MRm color camera (2.39 m/pixel). Thebright-field images were then filtered and resampled to match theresolution of the IR spectroscopic images. Since the same tissue wasimaged in both IR and bright-field, an affine transformation was thenapplied to bring all pixels into alignment. Transformation and alignmentwas performed using the GNU Image Manipulation Program (GIMP).

Tissue Preparation

Tissue samples were collected from anonymized, formalin-fixed, paraffinembedded breast biopsies taken with 0.6 mm and 1.6 mm needles. Clinicalsamples were obtained and prepared for infrared imaging were placed onlow-emissivity MirrIR coated glass slides from Kevley Technologies.Neighboring sections were placed on standard glass slides and stainedusing a BioGenex i6000 Automated Staining System.

Imaging

Unstained sections placed on low-emissivity glass were imaged using aPerkin-Elmer Spotlight 300 Mid-Infrared imaging system with a 16-elementlinear array detector. Imaging was performed in reflection mode at 4cm⁻¹ spectral resolution using a 15×, 0.5 NA objective, providing aspatial resolution of 6.25 μm. After imaging, the sections were stainedand imaged using a Zeiss Axiovert 200M fluorescence light microscopewith a 2× Plan-Neofluar 0.3NA objective providing a spatial resolutionof 1 μm. Color images were collected using an Axiocam HRC color CCDcamera. Neighboring histological sections used for qualitativevalidation were imaged using a Hamamatsu NanoZoomer.

Neural Network Training and Simulation

A mapping from principal components (spectral metrics) to RGB colorvalues was created using a neural network regression model. A 2-layerfeed-forward neural network was constructed. The output layer uses alinear transfer function and the input layer uses a nonlinear sigmoid(tan⁻¹) transfer function. The number of input nodes and internal nodesis specified by the user. The number of input nodes corresponds to thenumber of principal components (metrics) used in the regression.Increasing these values generally reduces error on the training data butcan reduce performance during validation. The spectra/RGB mapping of thetraining data provides a large number of training samples (severalmillion spectra), which permits an increase in the number of spectralfeatures while enforcing model generality and minimizing over-fitting.The resulting reduction in general performance is a form ofover-fitting, and minimizing the resulting generalization error iscommon practice in ANN training. The use of PCA in addition to a largenumber of training spectra can be used to improve ANN performance. Sincethe training data set is well represented using a small number ofprincipal components, the sample space for these parameters isrelatively small. For each stain, the most robust combination of inputand internal nodes was identified by searching this parameter space forthe best performance on an independent validation set.

For each stain a tissue array is sampled by constructing a training setof 600,000 to 2 million spectrum/RGB pairs. This set is divided into twogroups: 75% training and 25% validation. Training was performed usingLevenberg-Marquardt minimization based on the training set while thevalidation set was repeatedly checked to limit over-training. If themean-squared error of the validation set began to increase, training wasterminated. Several neural networks were trained, adjusting the numberof principal components and internal nodes. The second independentvalidation set is simulated on each network and the resulting meansquared error (MSE) is computed. The optimal network topology isselected by minimizing MSE performance.

For each stain, a slice of the data cube taken at the Amide-I peak (1650cm⁻¹) was taken and saved as a single two-dimensional image. Individualcores from the optical microscopy images were aligned to the Amide-Imosaic and resampled to match the resolution of the IR image. Since bothtissue samples were identical, only affine transformations werenecessary to achieve alignment between the IR and brightfield images.

In one examples of the method, IR spectra were then baseline correctedand normalized to Amide-I. The Amide-I value was stored for later use intraining. All spectra in a single training array were then used tocompute the mean spectrum mu and the principal component basis P. The 10largest principal components of the correlation matrix accounted forover 99% of the variance in the data set. These 10 components were usedas input values for a neural network. For stains that depended on tissuedensity for color, such as H&E and Masson's Trichrome, the originalAmide-I peak value taken before normalization (after baselinecorrection) was also used as an input feature.

For each stain, 1M sample spectra were randomly selected across 80cores. These samples, along with their corresponding brightfield colorvalues, were used to fit the following nonlinear neural network model:

W ₁ ^(T) tan h[W ₀ ^(T) [P ^(T)( s −μ)]+ b ₀ ]+b ₁ =C ₀

where the hyperbolic tangent is an element-wise operation on the vectorx;

${\tanh \; \overset{\_}{x}} = \begin{bmatrix}{\tanh \left( x_{0} \right)} \\{\tanh \left( x_{1} \right)} \\{\tanh \left( x_{2} \right)} \\\ldots \\{\tanh \left( x_{n} \right)}\end{bmatrix}$

This equation represents a neural network with a single hidden layer andthree output values corresponding to the color values of the matchingbrightfield image. The tensors W and b contain the weight and biasvalues for the hidden (0) and output (1) layers. The matrix P is theprincipal component basis and μ is the mean spectrum. 151 spectralmetrics were used as input features (W₀=[151×10]).

The weight and bias values were computed using Levenberg-Marquardt (LM)backpropagation, which is an iterative nonlinear fitting algorithm witha dynamic step size. The mean-squared error (MSE) between the computedoutput values and the corresponding brightfield color values was used asa performance metric for LM minimization. Over fitting was minimized byusing 25% of the original training pairs as a validation set. Thevalidation set was simulated after each iteration of LM. If the MSE ofthis validation set began to diverge, training was terminated.Qualitative validation was performed using cores independent from thetraining set.

Principal Component Analysis

Principal component analysis (PCA) was used to reduce the dimensionalityof the spectral data. The PCA transform was computed based on over 8million point spectra across a tissue microarray and surgical resection.Only pixels above a threshold absorption in the Amide I region of thespectrum (1650 cm⁻¹) were considered. Pixels with Amide I absorptionbelow this threshold were assumed empty and not considered in trainingor validation.

Since the first seven principal components represent over 99% of thevariance in the data, this optimization allows for dramatic reduction inthe amount of memory and time required to train the neural network. Inaddition, this reduces the parameter space required for regression,allowing a more robust mapping from point spectra to bright-field. Inthe next section, the method for finding the optimal number ofcomponents for each stain is discussed.

Bayesian Classification for Cell Type Identification

An 8-class modified Bayesian classifier was built in the same fashion aspreviously shown in prostate tissue (see Fernandez et al., NatureBiotechnol., 23, 469-474, 2005). Using the staining information, pixelscorresponding to epithelial cells, fibroblasts, collagen-rich stroma,lymphocytes, myofibroblasts, blood, necrosis, and mucin were identified.A training array consisting of 6 normal, 12 hyperplasia, 6 benign and 8in-situ and 36 cases of breast cancer had over 400,000 pixels identifiedfor the eight cell types. Average spectra were derived to identify keychemical differences between cell types. A total of 33 spectral metricswere found to be optimal for classification. Validation of theclassifier was performed on at least 100 samples of each type.

EXAMPLE 2 Comparison of H&E Stained Image to Computed Stain Image

Breast tissue previously fixed and paraffin embedded, was stained withH&E, and imaged using microscopy before and after staining with H&E. Asshown in FIG. 1A, brightfield optical image of tissue has littlecontrast. Conventionally, the use of H&E stain allows a visualization oftissue morphology using optical microscopy (FIG. 1B).

The same unstained tissue shown in FIG. 1A was subjected to IRspectroscopic imaging in the form of two spatial dimensions and aspectral dimension (FIG. 1C). The spectrum at every pixel containsspecific features that are indicative of the molecular content of thesample as well as its optical properties (Davis et al., Anal. Chem.82:3474-86, 2010).

The size of the data is then reduced from either knowledge ofbiochemical absorption or a statistically-based dimensionality reductionapproaches. This produces a data set containing a significantly smallernumber of spectral features that capture important variations in tissuechemistry. These features are then used as input for statistical patternrecognition algorithms (Bhargava, Anal Bioanal. Chem. 389:1155-69,2007).

Two methods for data reduction were examined. One based on biochemicalknowledge (metrics approach) (Bhargava et al., Biochim Biophys Acta.1758, 830-845, 2006) and another based on statistical terms (principalcomponent analysis). Both approaches provided equivalent results andresults using the metric approach are presented herein, since thecorresponding features are firmly based in tissue biochemistry.

The reduced data set formed an input to a neural network (NN) model(Haykin, S. S. Neural networks: a comprehensive foundation, PrenticeHall, 2007) that relates the biochemical input to molecular or dyeparameters. The NN transformed the recorded spectroscopic data at everypixel into color values or staining intensity found in histologicalstains imaged using bright-field microscopy (FIG. 1D). Finally, thepredicted stain or dye values were used to generate a computed stainimage (FIG. 1E). By comparing FIG. 1B (the H&E image) with FIG. 1E(computed stain image), it is shown that the computed stain imagefaithfully reproduces the staining pattern of H&E images that areimportant for recognition.

EXAMPLE 3 Comparison of Tissue-Specific Stained Images to Computed StainImages

H&E stains are fairly non-specific in terms of the functional ormolecular content of the tissue. Visualizing molecular content issignificantly more expensive, time-consuming and difficult; yet, this isprecisely the origin of the contrast mechanism in chemical imaging.Hence, additional stains were examined that are indicative of tissuefunction and integrity. Immunohistochemical (IHC) stains for cell typesare often used in diagnostic imaging or for specific research purposes,for example, high molecular weight (HMW) cytokeratin (epithelial-typecells), vimentin (fibroblast-like cells), smooth muscle alpha actin(myo-like cells), P63 (myoepithelial cells), CD31 (endothelial cells)and Masson's Trichrome stain (collagen and keratin fibers), are commonlyemployed.

A comparison of the physically stained and computationally stainedimages is shown in FIGS. 2A-2E. Since the process is correlative, duringtraining and validation, high throughput tissue microarrays (Kononen etal., Nat Med 4: 844-7, 1998) were used in which histopathologic,clinical and patient diversity is built-in. Depending on the stain typeand cellular abundance, the model was trained on 600,000 to 2 millionspectra from 96 patients and the approach validate=d in an independentset of 98 patients. As shown in FIGS. 2A-2E, the computationally stainedimages (the three images on the right) provide an accurate and reliablealternative to the use of images obtained from physically stainedtissues (the three images on the left).

EXAMPLE 4 Multiplexing Computed Stain Images

Using a combination of multiple staining results, it is possible tosubsequently deduce the cell types and/or molecular transformationspresent. However, in some cases, material is limited, thus limiting theamount of stains that can be used on any one sample. In contrast, thechemical content in spectroscopy, recorded once, can be used multipletimes to relate to expression levels in tissue. Hence, the method allowsnumerous (perhaps limitless) computed stains to be obtained from asingle infrared spectroscopic image for the same sample. Such acapability is useful when the tissue available is limited, e.g. in fineneedle biopsies or spheroids in 3D cell culture. In addition, thiscapability provides a quantitative method for histology, reducing oreliminating staining variance between samples and providing a means ofquickly extracting a large range of histological information from asingle tissue sample.

FIGS. 3A-3F show a panel of commons stains, as well as rare cell types,in a breast tissue needle biopsy Eliminating the need to stain can leadto faster availability of multiple stain-derived results in time-starvedsettings. Precious or limited samples can be imaged without perturbationand exploratory molecular staining is easily enabled. For example,intra-operative assessment is often required during surgery but isusually restricted to H&E staining. The availability of rapid stainingcan be accomplished to provide necessary histologic and molecularinformation. Sometimes, time pressures or variability in naturallyderived stains can lead to uneven staining (FIG. 3F, top). Computedstains are always of perfect quality (FIG. 3F, bottom) and providepristine images for further analysis, thus can replace conventionalapproaches and provide improved results. In larger surgical resections,different parts of the tissue may be examined easily and stains can beco-registered in the context of the overall architecture of the tissue(FIG. 3G). Thus, the computed histopathology approach is widelyapplicable and multiply useful in the gamut of pathology activities.

In view of the many possible embodiments to which the principles of thedisclosure may be applied, it should be recognized that the illustratedembodiments are only examples of the disclosure and should not be takenas limiting the scope of the invention. Rather, the scope of thedisclosure is defined by the following claims. I therefore claim as myinvention all that comes within the scope and spirit of these claims.

What is claimed is:
 1. A method of imaging a single unstained sample,comprising: obtaining a spectroscopic image of the single unstainedsample; reducing dimensionality of the spectroscopic image, therebygenerating reduced spectroscopic image data; and generating an outputcomputed stain image from the reduced spectroscopic image data using afeed-forward neural network trained using reduced spectroscopic datafrom a group of control samples stained with a target stain as input,wherein the feed-forward neural network generates bright-field colordata as output.
 2. The method of claim 1, wherein the spectroscopicimage of the single unstained sample is obtained using IR spectroscopicimaging instrumentation.
 3. The method of claim 1, wherein thespectroscopic images of the single unstained sample are obtained using aFourier transform infrared spectrometer.
 4. The method of claim 1,wherein obtaining the spectroscopic images of the single unstainedsample comprises obtaining two spatial dimensions and one spectraldimension.
 5. The method of claim 1, wherein reducing dimensionality ofthe spectroscopic image comprises use of principal component analysis(PCA) or a metrics approach.
 6. The method of claim 1, furthercomprising detecting one or more target proteins.
 7. The method of claim1, further comprising detecting one or more target pathogens.
 8. Themethod of claim 1, wherein the output computed stain image is comparableto an image obtained from staining the single unstained sample.
 9. Themethod of claim 1, wherein the single unstained sample is a biologicalsample from a mammal.
 10. The method of claim 9, wherein the singleunstained sample is known or suspected of comprising a tumor.
 11. Themethod of claim 10, wherein the tumor is breast, lung, renal, pancreas,prostate, colon, rectal, ovary, or liver cancer.
 12. The method of claim1, wherein the single unstained sample is from an environmental or foodsource.
 13. The method of claim 12, wherein the single unstained sampleis known or suspected of comprising a pathogen.
 14. The method of claim13, wherein the pathogen is a bacterium, virus, fungi, protozoa, orbacterial spore.
 15. The method of claim 1, wherein the single unstainedsample is a fixed, fresh or frozen tissue sample.
 16. The method ofclaim 1, further comprising selecting a subject having or suspected ofhaving cancer and obtaining a tissue sample from the subject.
 17. Anon-transitory computer-readable storage medium having instructionsthereon that, when executed by a processing system including aprocessor, facilitate performance of operations, comprising: obtaining aspectroscopic image of an unstained sample; reducing dimensionality ofthe spectroscopic image, thereby generating a reduced spectroscopicimage; and generating an output stain image from the reducedspectroscopic image computed based on comparing the reducedspectroscopic image to parameters from a neural network trained on acontrol sample, wherein the control sample is stained with a stainingprocess corresponding to the output stain image.
 18. The non-transitorycomputer-readable storage medium of claim 17, wherein reducing thedimensionality of the spectroscopic image is performed by ignoringparticular data of the spectroscopic image.
 19. The non-transitorycomputer-readable storage medium of claim 18, wherein the particulardata being ignored is determined according to pixels that do not satisfya threshold absorption.
 20. A device, comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the processing system, facilitate performance ofoperations, comprising: obtaining a spectroscopic image of an unstainedsample; reducing dimensionality of the spectroscopic image by ignoringparticular data of the spectroscopic image, thereby generating a reducedspectroscopic image; and generating an output computed stain image fromthe reduced spectroscopic image based on comparing the reducedspectroscopic image to output parameters of a feed-forward networktrained using spectroscopic images from a plurality of control samplesas input and associated bright-field color data of a staining processapplied to the control samples as output, wherein the output computedstain image is associated with the staining process.